The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The recent trend in video surveillance systems is to provide video analysis components that can detect potential threats from live streamed video surveillance data. The detection of potential threats assists a security operator, who monitors the live feed from many cameras, to detect actual threats.
Conventional surveillance systems detect potential threats based on predefined patterns. To operate, each camera requires an operator to manually configure abnormal behavior detection features. When the predetermined abnormal pattern is detected, the system generates an alarm. It often requires substantial efforts in adjusting the sensitivity of multiple detection rules defined to detect specific abnormal patterns such as speeding, against the flow, abnormal flow.
Such systems are inefficient in their operation. For example, the proper configuration of each camera is time consuming, requires professional help, and increases deployment costs. In addition, the definition and configuration of every possible abnormal behavior is not realistically possible due to the fact that there may just be too many to enumerate, to study, and to develop a satisfying solution in all possible contexts.